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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Research Project #441925
Research Project: Improving Crop Performance and Precision Irrigation Management in Semi-Arid Regions through Data-Driven Research, AI, and Integrated Models

Location: Water Management and Systems Research

Publications (Clicking on the reprint icon Reprint Icon will take you to the publication reprint.)

Weather data-centric prediction of maize non-stressed canopy temperature in semi-arid climates for irrigation management Reprint Icon - (Peer Reviewed Journal)
Nakabuye, H., Rudnick, D., DeJonge, K.C., Ascough, K.A., Liang, W., Lo, T., Franz, T., Qiao, X., Katimbo, A., Duan, J. 2023. Weather data-centric prediction of maize non-stressed canopy temperature in semi-arid climates for irrigation management. Irrigation Science. https://doi.org/10.1007/s00271-023-00863-w.

Can the Law of Constant Final Yield be used to delineate plant productivity limited by competition for water? - (Abstract Only)

Quantifying crop behavior with observations and models: Constraining simulations at LIRF - (Abstract Only)

Assessing CO2 exchange, water use and yield of maize crops under full and deficit irrigation using UAV and satellite imagery - (Abstract Only)

TSWIFT – A scanning tower-based hyperspectral instrument to capture diurnal and seasonal physiological plant response - (Abstract Only)

Spatiotemporal modeling of maize light extinction coefficient using Sentinel-2 multispectral data - (Abstract Only)

Soil Biology: Root form and function - (Book / Chapter)

Evaluation of artificial intelligence algorithms with sensor data assimilation in estimating crop evapotranspiration and crop water stress index for precision irrigation water management Reprint Icon - (Peer Reviewed Journal)
Katimbo, A., Rudnick, D.R., Zhang, J., Ge, Y., DeJonge, K.C., Franz, T.E., Shi, Y., Liang, W., Qiao, X., Heeren, D.M., Kabenge, I., Nakabuye, H.N., Duan, J. 2023. Evaluation of artificial intelligence algorithms with sensor data assimilation in estimating crop evapotranspiration and crop water stress index for precision irrigation water management. Smart Agricultural Technology. 4. Article e100176. https://doi.org/10.1016/j.atech.2023.100176.

Soil-plant hydraulics explain the stomatal efficiency-safety tradeoff Reprint Icon - (Peer Reviewed Journal)
Cai, G., Carminati, A., Gleason, S.M., Javaux, M., Ahmed, M. 2023. Soil-plant hydraulics explain the stomatal efficiency-safety tradeoff. Plant, Cell & Environment. https://doi.org/10.1111/pce.14536.

Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms Reprint Icon - (Peer Reviewed Journal)
Zhang, Y., Han, W., Zhang, H., Niu, X., Shao, G. 2023. Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms. Journal of Hydrology. Article e129086. https://doi.org/10.1016/j.jhydrol.2023.129086.

Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods Reprint Icon - (Peer Reviewed Journal)
Shao, G., Han, W., Zhang, H., Zhang, L., Wang, Y., Zhang, Y. 2022. Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods. Agricultural Water Management. 276. Article e108064. https://doi.org/10.1016/j.agwat.2022.108064.

Using remote sensing to inform crop coefficient based irrigation scheduling under deficit irrigation - (Abstract Only)

Improved estimation of crop water use by linking multi-scale thermal and solar-induced fluorescence measurements - (Abstract Only)

Optimizing soil moisture sensor depth for irrigation management using universal multiple linear regression - (Abstract Only)

Evaluating reflectance-based maize evapotranspiration modeling under different irrigation systems - (Abstract Only)